Saudi Arabian Oil Company

Otto E Meza Camargo

Tariq Mahmood

Marko Maucec

Waheed Syed Arshad

Antonio A Santagati

Marei Saeed Al-Garni

Ivan Deshenenkov

Babatunde Moriwawon

Edwin Caliboso

Khalid Hawas

Anwar Rahim Awan

Mustafa Amari

Ali A Taiban

Karla Olvera

Rakan Alkheliwi

Techniques for determining one or more hydrocarbon production zones in a subterranean reservoir include generating a 3D natural fracture model that includes a 3D discrete fracture network and a brittleness model for a subterranean formation into which a wellbore is formed; converting the 3D discrete fracture network into a 2D model to determine a continuous fracture density property; predicting a plurality of fluid-flow pathways using the continuous fracture density property modeled for each brittleness; hydraulic fracturing the wellbore to create one or more hydraulic fractures in the subterranean formation; subsequent to the hydraulic fracturing, logging the wellbore to determine one or more logged properties; identifying one or more fracture flow zones based on the one or more logged properties; and validating the predicted plurality of fluid-flow pathways based on the identified one or more fracture flow zones.

Determining hydrocarbon production zones in a subterranean reservoir

Systems, methods, and software can be used for identifying fracable areas. One example of a method includes receiving at least one of 3D petrophysical property and 3D geo-mechanical property in one or more areas. The method further includes receiving a model generating at least one hydraulic fracture parameter in a wellbore in the one or more areas. The method yet further includes training a machine learning model with at least one of the 3D petrophysical property and the 3D geo-mechanical property and the at least one hydraulic fracture parameter, and generating a fracability index for the wellbore based on the trained machine learning model.

Determining a three-dimensional fracability index for identifying fracable areas in a subsurface region

The present disclosure describes a computer-implemented method that includes: receiving a seismic dataset of a surveyed subsurface of a reservoir, the seismic dataset comprising observed pressure and production data of the reservoir as well as a set of geological and geo-mechanical parameters representing physical features of the surveyed subsurface; generating multiple realizations of a discrete fracture network (DFN) based on a subset of the set of geological and geo-mechanical parameters; selecting, from the multiple realizations, one or more realizations based on a parameter with a value under a 10% quantile of a full range of likely values; performing a forward simulation for the reservoir based on the selected one or more realizations and the observed pressure and production data; determining that a misfit of the forward simulation is below a threshold based on evaluating an objective function; and producing a model of the reservoir based on the forward simulation.

System and method to identify high-impact discrete fracture model realizations for accelerated calibration of reservoir simulation models

Images of subsurface formation walls adjacent well bores are obtained by the borehole imaging systems. The borehole images are processed to characterize the nature of fractures in the formation walls. The fractures are characterized as natural fractures or stress-induced tensile fractures based on the borehole image processing for geomechanical modeling in connection with reservoir characterization, fracture modeling, and stress analysis. A capability is provided to discriminate between the natural fractures and the stress induced tensile fractures and for performance of well activities.

Discrimination between subsurface formation natural fractures and stress induced tensile fractures based on borehole images

A methodology is provided to optimize the dynamic connectivity of a discrete fracture network (DFN) model of a subsurface reservoir against observed reservoir production measures using streamlines and machine learning. Adjustment of discrete fracture network properties of the reservoir is made locally and minimizes computer processing time spent in history matching. An iterative workflow identifies history match issues between measured and predicted or simulated water cut of reservoir produced fluids. Streamline analysis quantifies injector-producer communication and identifies reservoir grid block bundles that dominate dynamic response. A genetic algorithm updates discrete fracture network properties of the reservoir model to improve dynamic history match response.

Optimization of discrete fracture network (DFN) using streamlines and machine learning

Fracture porosity and permeability variations with increasing effective stress, are measured and determined by laboratory testing of rock cylindrical core plugs of various types (carbonate, silico-clastic, shale, e.g.) and dimensions. The testing can be implemented for both natural and induced fractures propagating axially in the rock core plug. The testing begins with initial testing, sample preparation and obtaining measurements on an intact plug to define matrix properties in the rock core plug. A rock core plug with defined matrix properties is then after further preparation, subjected to an axial shear fracture (natural or induced) propagating through its body. Measurements are then obtained from the plug with an axial shear fracture (natural or induced) propagating through its body to determine fracture properties.

Laboratory measurement of dynamic fracture porosity and permeability variations in rock core plug samples

Earth models of stress conditions in subsurface reservoirs are formed reflecting rock mechanical properties, stress regime and also wellbore stability. A number of variables related to the rock mechanical properties and stress conditions are parameterized. The variability of minimum horizontal stress magnitudes as a product of the relative contributions of the various rock properties and input parameters or variables used to form the model is analyzed. Differences between predicted minimum horizontal stress magnitudes in the formation and an actual measurement obtained from the formation (for example, fracture closure pressure) are determined. The determined differences are the minimized through an optimization methodology to find a best set of parameters suitable to match the minimum horizontal stress to actual formation measurement.

Determination of calibrated minimum horizontal stress magnitude using fracture closure pressure and multiple mechanical earth model realizations

Systems and methods for generating a fractured reservoir model include: receiving a seismic dataset of a surveyed subsurface; identifying a dynamic response of each parameter; selecting a subset of parameters from the set of parameters based on the dynamic response of each parameter; sampling an outer boundary of a parameter uncertainty domain; adjusting the range of values associated with each parameter of the subset based on the sampling; generating a geo-model based on the adjusted range of values associated with each parameter of the subset; generating a discrete fracture network model based on the geo-model; generating a scenario of a simulation model based on the discrete fracture network model and the geo-model; performing a forward simulation based on the scenario of the simulation model; determining that a misfit of the forward simulation is below a threshold by evaluating an objective function; and producing a model based on the forward simulation.

Method for dynamic calibration and simultaneous closed-loop inversion of simulation models of fractured reservoirs

In reservoir hydrocarbon exploration, fracture characteristics of subsurface reservoir formations are analyzed based on measures obtained about the subsurface formations and rock. Models of subsurface reservoirs are developed with predictions of natural fracture networks within the subject subsurface reservoirs. The mechanical properties of the formation rock in the reservoirs serve as a main controller to model the natural fractures distribution and their properties. The models so formed are important in the location and completion of wells for hydrocarbon exploration and production.

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Dhahran, Saudi Arabia

Otto E Meza Camargo